Graduate Thesis Or Dissertation


Bridge Inspection Using Bridge Information Modeling (BrIM) and Unmanned Aerial Systems (UASs) Public Deposited

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  • Bridge inspection is a critical task needed to monitor bridge quality and serviceability. In the U.S., 40% of bridges are more than 50 years old while most bridges are typically designed for a lifespan of 50 years. 9.1% of the 614,387 bridges across the U.S are considered structurally deficient. These statistics reported in the literature emphasize the urgent need for more frequent and comprehensive bridge inspections. However, the current manual inspection routine is expensive, time-consuming, hazardous, and subjective. Moreover, current Bridge Management Systems (BMS) may not coordinate management of all four phases of the bridge life cycle. Also, the dispersed inspection data drastically reduces the effectiveness of these systems. Therefore, there is a need to find cost-efficient and productive ways to inspect and manage our bridges. The objective of this study is to develop a novel framework for performing bridge inspections and management. The framework implements Bridge Information Modeling (BrIM) and Unmanned Aerial Systems (UASs) technologies to solve the problems that identified in the current manual bridge inspection and management practice. The proposed framework was implemented using data collected from an existing bridge located in Eugene, Oregon. Different types of defects were identified from the digital images captured by the UAS, and cracks were detected automatically by applying computer vision algorithms to those images. The identified defects were assigned to individual BrIM elements. BrIM was used as the central database to store the 3D bridge model and all inspection data. The framework also enables bridge inspectors and decision makers to access the most up-to-date inspection data simultaneously by taking advantage of cloud computing technology. The proposed framework: (1) provides a systematic approach for collecting and accurately documenting the structural condition assessment data, (2) reduces the number of site visits and eliminates potential errors resulting from data transcription, and (3) enables a more efficient, cost-effective and safer bridge inspection process.
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Peer Reviewed
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  • Ongoing Research
Embargo date range
  • 2018-06-18 to 2019-07-19



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